About: Using EpiRank for Analyzing Emerging Coronavirus Epidemic Patterns

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Using EpiRank for Analyzing Emerging Coronavirus Epidemic Patterns
Creator	Chien, Wei Chin, Benny Huang, Chung-Yuan
Source	MedRxiv
abstract	Huang et al. (2019) used their EpiRank algorithm, which emphasizes forward-and-backward commuter flow between homes and workplaces, to analyze the distribution patterns of two infectious diseases in Taiwan: the 2009-H1N1 influenza virus and the widespread emergence of the 2000-2008 type 71 enterovirus (EV). As this article was being prepared, the spreading mechanism of the novel coronavirus disease now designated as COVID-19 had yet to be identified, but according to the American Centers for Disease Control, its spreading mechanism and patterns are likely more similar to influenza than to other coronaviruses such as Severe Acute Respiratory Syndrome (SARS-CoV-1) or Middle East Respiratory Syndrome (MERS-CoV). To consider potential COVID-19 spatial patterns, we applied EpiRank to the 2003 SARS outbreak in north Taiwan for comparison with H1N1 and EV. SARS was found to be less contagious than H1N1 or EV, but with a significantly higher fatality rate. The characteristics of these diseases determined their specific spatial spreading patterns, as reflected in the different effects of forward and backward commuting movement. Our motivation is to highlight these differences and to illustrate EpiRank spatial patterns for the 2003 SARS outbreak for comparison with EpiRank-determined distributions for the H1N1 and EV outbreaks. Our results indicate that the daytime parameter (i.e., forward movement effect) range was slightly higher (0.5-0.55) for the SARS outbreak than for either the influenza (0.4-0.5) or EV (0.3-0.5) outbreaks, suggesting that the forward-and-backward movements of individuals between residential and core urban areas with concentrated populations were equally important regarding the spread of SARS. While COVID-19 might resemble either SARS or H1N1 in terms of spatial spreading, its daytime parameter is likely somewhere in-between, with backward movement being dominant (similar to H1N1) or with forward and backward movement being equally important (similar to SARS). Building on Huang et al. (2019) paper, we present an estimated risk distribution pattern for the Taipei Metropolitan Area for a daytime parameter of 0.55.
has issue date	2020-04-24(xsd:dateTime)
bibo:doi	10.1101/2020.04.20.20072736
has license	medrxiv
sha1sum (hex)	bb39272447a26f8a2c49255719438bf15bc3caa1
schema:url	https://doi.org/10.1101/2020.04.20.20072736
resource representing a document's title	Using EpiRank for Analyzing Emerging Coronavirus Epidemic Patterns
resource representing a document's body	covid:bb39272447a26f8a2c49255719438bf15bc3caa1#body_text
is schema:about of	named entity 'COVID-19' named entity 'outbreaks' named entity 'outbreaks' named entity 'patterns' named entity 'Disease' named entity 'Coronavirus' named entity 'NORTH' named entity 'SPREAD' named entity 'HOMES' named entity 'ENTEROVIRUS' named entity 'COMMUTING' named entity 'TERMS' named entity 'ALGORITHM' named entity '2000' named entity 'BUILDING' named entity 'virus' named entity 'H1N1' named entity 'public health' named entity 'Taipei' named entity 'virus' named entity 'forward movement' named entity 'SARS' named entity 'sensitivity analysis' named entity 'SARS' named entity 'COVID' named entity 'fatality rate' named entity 'flu' named entity 'spatial epidemiology' named entity 'SARS outbreak' named entity 'Spearman's rho' named entity 'infection' named entity 'SARS' named entity '2009 H1N1' named entity 'influenza' named entity 'Xinzhuang' named entity 'SARS' named entity 'medRxiv' named entity 'flu' named entity 'public transportation' named entity 'influenza' named entity 'H1N1' named entity 'patterns' named entity 'daytime' named entity 'resemble' named entity 'patterns' named entity 'distribution' named entity 'Asymmetric' named entity 'Taiwan' named entity 'coronaviruses' named entity 'MERS-CoV' named entity 'H1N1 influenza virus' named entity 'H1N1' named entity '2003 SARS outbreak' named entity 'Centers for Disease Control' named entity 'fatality rate' named entity 'Coronavirus Epidemic' named entity 'Taiwan' named entity 'influenza' named entity 'Taiwan' named entity 'network flow' named entity 'SARS' named entity 'SARS' named entity 'flu' named entity 'COVID' named entity 'medRxiv' named entity 'SARS' named entity 'greater media' named entity 'epidemiological' named entity 'epidemiologic' named entity 'New Taipei City' named entity 'Taiwan' named entity 'Python 3' named entity 'probability' named entity 'virological' named entity 'preventive measures' named entity 'flu' named entity 'Spearman's rho' named entity 'COVID' named entity 'uniform distribution' named entity 'damping factor' named entity 'virology' named entity 'influenza' named entity 'SARS'

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